CN111189424B - Road data bending degree detection method based on multistage search radius - Google Patents
Road data bending degree detection method based on multistage search radius Download PDFInfo
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Abstract
The invention discloses a road data bending degree detection method based on multistage search radius, which comprises the following steps: 1) a plane coordinate system is appointed, and node position information of road data is read; 2) determining a multi-level search radius set according to conditions such as functions and levels of roads; 3) respectively detecting the bending degree of the road data by using the multi-stage search radius, and identifying the part with the larger bending degree in the road data; 4) and comprehensively evaluating the local analysis result of each road, and screening the road with larger bending degree. The method can be used for quickly, accurately and efficiently detecting the bending degree of the road alignment, identifying the part with larger bending degree in the road, quantitatively describing the bending degree of the road, and performing alignment detection on road data, thereby reducing the workload of manual inspection, improving the accuracy of alignment detection, providing reference for reconstruction and extension of the road, and practically improving the traffic capacity and service level of the road.
Description
Technical Field
The invention relates to the technical field of road alignment detection, in particular to a road data bending degree detection method based on a multi-stage search radius.
Background
People's production and life are dependent on the normal operation of traffic systems, and roads play a crucial role as the skeleton of the traffic systems. According to the overall planning of a road network, roads are divided into different functional categories, the roads with higher functional categories have higher design speed, longer design service life, stronger design traffic capacity and larger design traffic volume, the roads with lower functional categories have lower design speed, shorter design service life, weaker design traffic capacity and smaller design traffic volume. After determining the functional category and the design speed of the road, all the factors of the road, such as the radius of a flat curve, the sight distance, the longitudinal slope and the like, need to be matched with the road to obtain a continuous and balanced design. The road alignment is scientific and reasonable, the traffic capacity and the service level of the road are improved, the comfort of drivers and passengers is ensured, and the occurrence rate of traffic accidents is reduced. The plane line shape of the road is generally formed by combining three line-shaped elements of a straight line, a circular curve and a gentle curve, wherein the circular curve and the gentle curve are collectively called as a road flat curve. Unreasonable line shapes of roads often form potential safety hazards, so that the accident rate is increased, if the straight line parts are too long, on one hand, the construction cost is too high, and on the other hand, overspeed driving and psychological relaxation and vigilance of drivers can be induced, so that the driving safety is influenced; and if the road bending degree is too large, the problems of too short sight distance, difficult meeting and the like can be caused, and potential safety hazards are formed. In the road linear design, when entering a section with a better terrain condition from the section with the better terrain condition, the linear technical indexes are gradually transited to prevent sudden change, and in order to realize smooth connection transition between a straight line and a circular curve in a road flat curve, a relaxation curve is arranged at the connection part of the straight line and the circular curve for connection, so that the requirements on safety, vision, landscape and the like in the road design are met. Due to the linear requirement of the road, the road is subjected to bending degree detection, the part with large bending degree is reconstructed and expanded, the traffic capacity and the service level of the road are improved, the safety of drivers and passengers is ensured, the 'smooth people and smooth things' are realized, the development requirements of 'comprehensive traffic' and 'safe traffic' are met, and the method is an important task in road data quality evaluation.
The road data bending degree detection has great reference value for potential safety hazard detection, dangerous road section investigation, road reconstruction and expansion and the like, the existing road bending degree detection is usually realized through on-site flat curve measurement, the measurement and the setting period are long, piles need to be repeatedly set, the limitation of terrain and weather is easily caused, a large amount of manpower, financial resources and material resources are consumed, and random errors in the measurement cannot be avoided. If can realize automatic or semi-automatization's road curvature detects, carry out preliminary detection to the curvature of road, select the great road of curvature, reduce the screening scope of the linear problem of road, carry out the manual work to the road with the remote sensing image and check or carry out the flat curve survey on the spot and establish, can show the efficiency that improves linear detection, reduce the cost that linear detection, be favorable to the road to reform and expand the effective propulsion of work, realize the linear continuation of road, the equilibrium, ensure the safety of traveling, it is comfortable, the operation efficiency of promotion road network.
Disclosure of Invention
The invention aims to provide a road data curvature degree detection method based on a multi-level search radius, which can be used for quantitatively checking the curvature degree of a road, quantitatively evaluating the curvature degree of the road, and remarkably improving the road data alignment detection efficiency so as to solve the problems in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme:
a road data bending degree detection method based on multi-stage search radius comprises the following steps:
step 1): reading node position information of road data: appointing a plane coordinate system, reading in node position information describing road route shape information, and respectively recording the nodes as A0，A1，A2…AnThe node coordinates are respectively denoted as (x)0，y0)，(x1，y1)…(xn，yn)；
Step 2): according to the conditions of road functions, levels and the like, the aim and the standard of linear detection are combined to determine the multi-level search radius, and the set of the search radii is recorded as { R0，R1，R2…RnSearching radius unit of meter, and thresholding radius of circular curveIs denoted by RTHIn meters;
step 3): respectively detecting the bending degree of the road by using multi-stage search radiuses, and marking the part with the excessive bending degree;
step 4): and (4) performing comprehensive evaluation by combining the local analysis result of each road, and screening the road with larger bending degree.
Further, the step 3) specifically comprises:
step 301): selecting a search radius R from the search radius set determined in the step 2), wherein the unit is meter;
step 302): for the selected road ProadIf the total number of the nodes is less than 3, the road is a straight line and does not need to be checked for the bending degree; if the total number of nodes of the road is not less than 3, every adjacent 3 nodes Ai、Ai+1、Ai+2Forming a bending degree detection unit, wherein the total number of the bending degree detection units is n-2 for the road with the total number of nodes being n;
step 303): adjusting the bending degree detection unit generated in step 302), wherein the node Ai、Ai+1Position is fixed if Ai+2And Ai+1Is greater than R, then A isi+2A third node as a detection unit, otherwise along the advancing direction of the road, Ai+2Next node a ofi+3As Ai+2Up to Ai+2And Ai+1Is greater than R, node Ai、Ai+1、Ai+2Respectively is (x)i，yi)，(xi+1，yi+1)，(xi+2，yi+2) The distance judgment formula is (x)i+2-xi+1)2+(yi+2-yi+1)2≥R2；
Step 304): quantitatively calculating the degree of bending of each detection unit generated in step 303), and setting AiAi+1、Ai+1Ai+2The transition curve between the two sides is composed of a relief curve, a circular curve and a relief curve, and the length ratio is 1: 1: 1, flat curvedThe radius is reduced from infinity to the radius of the circular curve and then increased to infinity, the trajectory equation of the easement curve and the circular curve is determined, and the length L of the circular curve is calculatedcircleAnd the corresponding central angle alpha, calculating the radius of the circular curve according to the central angle alpha, and comparing the radius with the circular curve radius threshold value R set in the step twoTHAnd comparing, and marking the nodes with the radius of the circular curve smaller than the threshold as the nodes with larger bending degree.
Further, the step 4) specifically includes:
step 401): establishing two fields N in road datatotal，NcurveRespectively representing the total number of nodes in the road data and the total number of nodes with larger curvature in the road data, and dividing N into NcurveIs set to THcurve；
Step 402): counting the total number of nodes in the road data, and recording the information into NtotalIn a field;
step 403): counting the total number of nodes with excessive bending degree in the road data, and recording the information into NcurveIn a field.
Compared with the prior art, the invention has the beneficial effects that:
1. the road data bending degree detection method based on the multi-stage search radius provided by the invention has the advantages that the calculation process is quick and efficient, and the problem road sections can be clearly identified according to a uniform detection standard.
2. According to the road data bending degree detection method based on the multistage search radii, the search radii are flexibly adjusted, so that different conditions can be comprehensively covered, a road with larger bending degree on a small scale can be effectively identified by a smaller search radius, a road with larger bending degree on a large scale can be effectively identified by a larger search radius, the comprehensiveness and reliability of detection results can be ensured by reasonably setting the combination of the search radii, and the classified statistics and display of the bent road sections on different scales can be carried out.
3. According to the road data bending degree detection method based on the multistage search radius, provided by the invention, the bending degree can be detected as long as the node position information of the road data exists, the real-time detection is not needed, the labor, the material and the financial resources are saved, the screening range can be narrowed for the manual inspection of the remote sensing image and the real-time flat curve detection, and the task load of the subsequent inspection work is reduced.
Drawings
FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is a node map showing road data;
FIG. 3 shows a shape diagram of a road flat curve;
FIG. 4 is a partial view of a locally steep overall smoothness in a road;
FIG. 5 is a diagram of a portion of a road where the local smoothness is entirely steep;
fig. 6 is a diagram showing an adjustment method of the curvature detecting means.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, in the embodiment of the present invention: the method for detecting the road data bending degree based on the multi-stage search radius comprises the following steps:
the method comprises the following steps: reading node position information of road data:
appointing a plane coordinate system, reading in node position information describing road alignment information, and respectively recording the nodes as A0，A1，A2…AnAnd the node coordinates are respectively marked as (x)0，y0)，(x1，y1)…(xn，yn) As shown in fig. 2;
step two: determining a multi-level search radius:
according to the conditions of road function, grade and the like, the linear detection is combinedPurpose and standard, determining multi-level search radius, and recording the set of search radii as { R0，R1，R2…RnThe unit of the search radius is meter, and the radius threshold of the circular curve is recorded as RTHIn meters;
as shown in fig. 4, the local steep and overall smooth road section is suitable for a small-scale search radius;
as shown in fig. 5, the local smooth and overall steep road section is suitable for large-scale search radius; the curved road sections on different scales are difficult to screen by using a single search radius, and the curved road sections on different scales can be effectively identified by using different search radii according to the characteristics of various roads of different types, so that the comprehensiveness and the reliability of a curved degree detection result are ensured;
step three: respectively detecting the bending degree of the road by using multi-stage search radiuses, and marking the part with the excessive bending degree;
step 301): selecting a search radius R from the search radius set determined in the step two, wherein the unit of the search radius R is meter;
step 302): selecting a road P in the data setroadReading all node position information in the road data, and if the total number of the nodes is less than 3, the road is a straight line and does not need to be checked for the bending degree; if the total number of nodes of the road is not less than 3, every adjacent 3 nodes Ai、Ai+1、Ai+2Forming a bending degree detection unit, wherein the total number of the bending degree detection units is n-2 for the road with the total number of nodes being n;
step 303): adjusting the bending degree detection unit generated in step 302), wherein the node Ai、Ai+1Position is fixed if Ai+2And Ai+1Is greater than R, then A isi+2A third node as a detection unit, otherwise along the advancing direction of the road, Ai+2Next node a ofi+3As Ai+2Up to Ai+2And Ai+1Is greater than R, node Ai、Ai+1、Ai+2Respectively is (x)i，yi)，(xi+1，yi+1)，(xi+2，yi+2) The distance judgment formula is (x)i+2-xi+1)2+(yi+2-yi+1)2≥R2(ii) a The adjustment process is shown in fig. 6;
step 304): for the curve degree detection unit generated in the previous step, the latest version of road route design specification (JTG D20-2017) indicates that "road plane line shape is composed of three line shape elements of a straight line, a circular curve, and a gentle curve", so a is set as aiAi+1、Ai+1Ai+2The transition curve between the two sides is composed of a relief curve, a circular curve and a relief curve, and the length ratio is 1: 1: 1, as shown in fig. 3, curve AE is a first gentle curve, curve EF is a circular curve, curve CF is a second gentle curve, the curvature radius gradually decreases from infinity during the sliding from a to E, the curvature radius remains unchanged during the sliding from E to F, and the curvature radius gradually increases to infinity during the sliding from F to C;
the first relaxation curve satisfies:
c＝R*l
wherein R is the radius of the curve, l is the total length of the easement curve, c is the parameter of the convolution, x is the abscissa value of any point on the easement curve, y is the ordinate value of any point on the easement curve, point A is set as the origin of coordinates, and a straight line is set as the origin of coordinates
Is the x-axis;
c is a constant, the smaller the value of c, the steeper the relaxation curve, and the larger the value of c, the smoother the line; the larger the value c is, the more attractive the road alignment is, but the too large value c can cause the too long convolution line, which affects the driving experience and is not beneficial to the road surface drainage, therefore, the method adopts a compromise mode, and defines c as:
in the formula, LABIs node Ai、Ai+1A distance between A and AiHas the coordinates of (x)i，yi),Ai+1Has the coordinates of (x)i+1，yi+1) Beta is a vector AiAi+1Sum vector Ai+1Ai+2The angle of deflection therebetween;
the coordinate value of the E point is set as (x)0，y0) The included angle between the tangent line of the curve at the point E and AB is alpha0Then the circular curve satisfies:
wherein R is the radius of the circular curve, l is the length of the circular curve, x is the abscissa value of any point on the circular curve, and y is the ordinate value of any point on the circular curve;
in fig. 3, the length of curve AC is approximately equal to the length L of a circular curve tangent to line AB at point a and to line BC at point CcircleThen L iscircleSatisfies the following conditions:
Lcircle＝r*β
in the formula, LABIs the length of the line segment AB and beta is LcircleThe corresponding central angle r is the radius of the circular curve; the curve between the ACs is composed of a relaxation curve, a circular curve, and a relaxation curve, and the length ratio is 1: 1: 1, the length of the circular curve can be approximately expressed as:
as shown in fig. 3, α represents a central angle corresponding to a circular curve, and it can be seen that:
wherein:
therefore, the radius of the circular curve can be calculated, and the calculation formula of the radius of the circular curve is as follows:
the radius R of the circular curve is compared with a radius threshold value RTHMaking comparison, R is less than RTHThe points are marked, so that the parts with larger bending degree in the road can be accurately and effectively identified, and the flat curve radius r can be used for describing the bending degree of the detection unit in a quantitative mode.
Step four: comprehensively evaluating the local analysis result of each road to screen out the road with larger bending degree, which specifically comprises
Step 401): establishing two fields N in road datatotal，NcurveRespectively representing the total number of nodes in the road data and the degree of curvature in the road dataLarge total number of nodes, NcurveIs set to THcurve；
Step 402): counting the total number of nodes in the road data, and recording the information into NtotalIn a field;
step 403): counting the total number of nodes with excessive bending degree in the road data, and recording the information into NcurveIn a field.
In summary, the following steps: according to the road data bending degree detection method based on the multi-stage search radius, the detection result can be used for accurately positioning the road data with larger bending degree, and according to N in the attribute field of the road datacurveThe value of the field can be quantitatively evaluated, so that reference is provided for further manual verification and field flat curve measurement and establishment by using remote sensing images, suggestions are provided for reconstruction and expansion work of the road, the traffic capacity and the service level of the road are improved, the operation efficiency of a road network is improved, and the safety and the comfort of drivers and passengers in the driving process are guaranteed.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the equivalent replacement or change according to the technical solution and the inventive concept of the present invention should be covered by the scope of the present invention.
Claims (2)
1. A road data bending degree detection method based on multi-stage search radius is characterized by comprising the following steps:
step 1): reading node position information of road data: appointing a plane coordinate system, reading in node position information describing road alignment information, and respectively recording the nodes as A0，A1，A2…AnAnd the node coordinates are respectively marked as (x)0，y0)，(x1，y1)…(xn，yn)；
Step 2): determining multi-stage search according to road function and grade condition and alignment detection purpose and standardThe search radius is set as { R }0，R1，R2…RnThe unit of the search radius is meter, and the radius threshold of the circular curve is recorded as RTHIn meters;
step 3): respectively detecting the bending degree of the road by using multi-stage search radiuses, and marking the part with the excessive bending degree;
step 4): comprehensively evaluating the local analysis result of each road, and screening out the road with larger bending degree;
the step 3) specifically comprises the following steps:
step 301): selecting a search radius R from the search radius set determined in the step 2), wherein the unit is meter;
step 302): for the selected road ProadIf the total number of the nodes is less than 3, the road is a straight line and does not need to be checked for the bending degree; if the total number of nodes of the road is not less than 3, every adjacent 3 nodes Ai、Ai+1、Ai+2Forming a bending degree detection unit, wherein the total number of the bending degree detection units is n-2 for the road with the total number of nodes being n;
step 303): adjusting the bending degree detection unit generated in step 302), wherein the node Ai、Ai+1Position is fixed if Ai+2And Ai+1Is greater than R, then A isi+2A third node as a detection unit, otherwise along the advancing direction of the road, Ai+2Next node a ofi+3As Ai+2Up to Ai+2And Ai+1Is greater than R, node Ai、Ai+1、Ai+2Respectively is (x)i，yi)，(xi+1，yi+1)，(xi+2，yi+2) The distance judgment formula is (x)i+2-xi+1)2+(yi+2-yi+1)2≥R2；
Step 304): quantitatively calculating the degree of bending of each detection unit generated in step 303), and setting AiAi+1、Ai+1Ai+2The transition curve between two sides is composed of a relaxation curve, a round curve and a relaxation curve, the length ratio is 1: 1, the radius of the flat curve is reduced from infinity to the radius of the round curve and then is increased to infinity, the trajectory equation of the relaxation curve and the round curve is determined, and the length L of the round curve is calculatedcircleAnd the corresponding central angle alpha, calculating the radius of the circular curve according to the central angle alpha, and comparing the radius with the radius threshold value R of the circular curve set in the step twoTHAnd comparing, and marking the nodes with the radius of the circular curve smaller than the threshold as the nodes with larger bending degree.
2. The method for detecting the degree of curvature of road data based on multi-level search radii as claimed in claim 1, wherein the step 4) specifically comprises:
step 401): establishing two fields N in road datatotal，NcurveRespectively representing the total number of nodes in the road data and the total number of nodes with larger curvature in the road data, and dividing N into NcurveIs set to THcurve；
Step 402): counting the total number of nodes in the road data, and recording the information into NtotalIn a field;
step 403): counting the total number of nodes with excessive bending degree in the road data, and recording the information into NcurveIn a field.
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Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP0789225A1 (en) * | 1996-02-07 | 1997-08-13 | Volkswagen Aktiengesellschaft | Method for determining road curvature from digitally stored map information |
| JP2006318044A (en) * | 2005-05-10 | 2006-11-24 | Toyota Motor Corp | Road curvature detection device and road curve radius detection device |
| CN102201035A (en) * | 2011-05-12 | 2011-09-28 | 北京星河易达科技有限公司 | Estimation method capable of calculating bending/gradient of road ahead |
| CN102706291A (en) * | 2012-05-18 | 2012-10-03 | 长安大学 | Method for automatically measuring road curvature radius |
| CN102831261A (en) * | 2012-07-31 | 2012-12-19 | 吉林大学 | Modeling method of complex road |
| CN103310699A (en) * | 2012-03-16 | 2013-09-18 | 北京四维图新科技股份有限公司 | Method for extracting road alignment parameters |
| CN105373689A (en) * | 2014-08-21 | 2016-03-02 | 北京协进科技发展有限公司 | Method and device for determining road curvature in electronic map |
| CN105740505A (en) * | 2016-01-21 | 2016-07-06 | 东南大学 | GPS-RTK technology based road space line shape recovery method |
| CN106156267A (en) * | 2016-02-04 | 2016-11-23 | 东南大学 | A kind of track stage enhancement type vector numerical map manufacture method towards highway |
| CN107727045A (en) * | 2017-09-30 | 2018-02-23 | 福建农林大学 | Road radius of horizontal curve measuring method based on wheelpath |
| CN108229803A (en) * | 2017-12-25 | 2018-06-29 | 中公高科养护科技股份有限公司 | A kind of extracting method in feature section, the judgment method of Dangerous Area and system |
| CN108645342A (en) * | 2018-04-25 | 2018-10-12 | 国交空间信息技术（北京）有限公司 | A kind of road width extracting method based on path locus and high resolution image |
| CN109033176A (en) * | 2018-06-25 | 2018-12-18 | 腾讯科技（深圳）有限公司 | Determination method, apparatus, storage medium and the computer equipment of road curvature |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9208601B2 (en) * | 2012-06-10 | 2015-12-08 | Apple Inc. | Computing plausible road surfaces in 3D from 2D geometry |
-
2019
- 2019-12-27 CN CN201911375605.8A patent/CN111189424B/en active Active
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP0789225A1 (en) * | 1996-02-07 | 1997-08-13 | Volkswagen Aktiengesellschaft | Method for determining road curvature from digitally stored map information |
| JP2006318044A (en) * | 2005-05-10 | 2006-11-24 | Toyota Motor Corp | Road curvature detection device and road curve radius detection device |
| CN102201035A (en) * | 2011-05-12 | 2011-09-28 | 北京星河易达科技有限公司 | Estimation method capable of calculating bending/gradient of road ahead |
| CN103310699A (en) * | 2012-03-16 | 2013-09-18 | 北京四维图新科技股份有限公司 | Method for extracting road alignment parameters |
| CN102706291A (en) * | 2012-05-18 | 2012-10-03 | 长安大学 | Method for automatically measuring road curvature radius |
| CN102831261A (en) * | 2012-07-31 | 2012-12-19 | 吉林大学 | Modeling method of complex road |
| CN105373689A (en) * | 2014-08-21 | 2016-03-02 | 北京协进科技发展有限公司 | Method and device for determining road curvature in electronic map |
| CN105740505A (en) * | 2016-01-21 | 2016-07-06 | 东南大学 | GPS-RTK technology based road space line shape recovery method |
| CN106156267A (en) * | 2016-02-04 | 2016-11-23 | 东南大学 | A kind of track stage enhancement type vector numerical map manufacture method towards highway |
| CN107727045A (en) * | 2017-09-30 | 2018-02-23 | 福建农林大学 | Road radius of horizontal curve measuring method based on wheelpath |
| CN108229803A (en) * | 2017-12-25 | 2018-06-29 | 中公高科养护科技股份有限公司 | A kind of extracting method in feature section, the judgment method of Dangerous Area and system |
| CN108645342A (en) * | 2018-04-25 | 2018-10-12 | 国交空间信息技术（北京）有限公司 | A kind of road width extracting method based on path locus and high resolution image |
| CN109033176A (en) * | 2018-06-25 | 2018-12-18 | 腾讯科技（深圳）有限公司 | Determination method, apparatus, storage medium and the computer equipment of road curvature |
Non-Patent Citations (1)
| Title |
|---|
| 《道路平面线形拟合方法比较研究》;张航 等;《武汉理工大学学报（交通科学与工程版）》;20180815;第42卷(第4期);第594-598页 * |
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